Web analytics is the measurement, collection, analysis and reporting of the traffic data of a web site for purposes such as understanding and optimizing web site usage. The traffic data is typically organized in the form of one or more multidimensional datasets whose metadata may include multiple dimensions and metric attributes (also known as “measures”). Conventional approaches typically generate multiple (sometimes hundreds of) reports by focusing on the factual aspects of the web traffic, e.g., by visualizing different subsets of a multidimensional dataset defined by various configurations of dimensions and metric attributes. From examining the visualized traffic data, a web analyst may be able to discover useful information for improving the quality and volume of the traffic to the web site. But this exercise of searching for useful information within the multidimensional dataset is non-trivial especially if the volume of the traffic data is significant or the metadata includes a large number of dimensions and metric attributes that may correspond to hundreds or even thousands of configurations. Because different configurations correspond to different factual aspects of the dataset, it is difficult to rank the configurations by their respective importance to the web analyst based on a well-accepted standard.